Pipelined filter ordering is a central problem in database query optimization, and has received renewed attention recently in the context of environments such as the web, continuous high-speed data streams and sensor networks. We present algorithms for two natural extensions of the classical pipelined filter ordering problem: (1) a distributional type problem where the filters run in parallel and the goal is to maximize throughput, and (2) an adversarial type problem where the goal is to minimize the expected value of multiplicative regret. We show that both problems can be solved using similar flow algorithms, which find an optimal ordering scheme in time O(n2), where n is the number of filters. Our algorithm for (1) improves on an earlier O(n3 log n) algorithm of Kodialam.